These days, AI is expected to drive worldwide revenues from nearly $8 billion in 2016 to more than $47 billion in 2020, across a broad range of industries. There is no doubt that AI has changed the way we run business.
Take a case of Makoto Koike as an example. It takes his mother 8 hours each day to sort cucumbers from the family farm into different categories at the peak of the Japanese harvest. This is such a boring, time-wasting task that he finally made a decision to using automation. Makoto started off with TensorFlow, a popular open-source machine learning framework Despite not being a machine learning expert. Sorting by size, shape and other attributes, the system has an accuracy rate of around 75 %. This proved how AI could transform even the smallest family-run business sooner or later.
Large companies like Google, Apple are well-aware of this transformative power. Most Fortune 500 companies also have dedicated AI teams in place. However, for smaller and medium enterprises in the same situation as Makoto, they have faced difficulties in exploring how AI can hone their business because of the lack of expertise.
Those companies are in need of preparing huge data sets and spend putting their sources into computing power to analyze them, although having ability to employ AI experts However, the big cloud providers come up with solutions to these issues.
As a service or cloud AI, Machine learning is now a major part of cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and IBM Cloud. To add AI to business applications, these companies supply their customers with access to pretrained deep learning models—for image recognition. Simplifying the process of building, training and deploying, tools customized models on the cloud.
From Chris Nicholson’s judgment - CEO of Skymind, there are tools data scientists knowing how to code and for software developers not knowing how to properly tune algorithms but knowing to create apps if receiving an API to code against. In addition to this, there are tools for clickers basically relating through GUIs, which covers the enormous number of people in all over the world.
While the likes of Amazon Rekognition and Google Translation are APIs created around pretrained models, Microsoft Azure ML Studio, Amazon SageMaker, and Google Cloud ML Engine are broadly similar platforms sitting more toward the data scientist end of the spectrum, helping deep learning experts to train, tune, and deploy their models at scale.
To solve a specific business problem, which pretrained models may cannot tackle, deep learning is typically used. This is the issue of the latter approach. Put it in another way, if you want it to identify different types of cucumbers, using an API to recognize different breeds of kittens is bad.
Now you can use a bunch of data on which we trained a model to make predictions about images but that’s a false solution in the sense that it’s still just as hard and just as necessary to train a model on your own data if wanting to customize that solution.
To bridge the gap between highly customized neural networks and the more basic one-size-fits-all pretrained models, launched by Google, Cloud AutoML uses customer data to automatically create a custom deep learning model. Allowing users to built custom machine learning models for image recognition via a drag-and-drop interface, Cloud AutoML Vision is the first release from the new service.
Disney is one of large companies using the tool and it helps Disney’s customers to search its merchandise for particular Disney characters, even the product with no the character’s name. However, preparing their own data for the AutoML service is still a difficulty for some enterprises.
To attract more customers, the company is definitely trying to leverage its AI expertise as Google’s cloud business lies a distant third behind AWS and Microsoft Azure. But given the computational resources needed to train and deploy deep learning models, all the major cloud vendors stand to make considerable sums from renting chips for this purpose.
The proportion of commercial enterprise using AI will be 75% by 2021 according to IDC. There is no doubt that Machine learning tools will become an essential component of any cloud computing service. To make the most of AI, companies need to offer these kinds of capabilities.
Determining about whether a business case is clear for using deep learning tools is a needed task enterprises should do at first. People are much less likely to succeed If not defining the specific problem they want to solve. People are using it in a very specific way to cope with a very specific problem.
Take a case of Makoto Koike as an example. It takes his mother 8 hours each day to sort cucumbers from the family farm into different categories at the peak of the Japanese harvest. This is such a boring, time-wasting task that he finally made a decision to using automation. Makoto started off with TensorFlow, a popular open-source machine learning framework Despite not being a machine learning expert. Sorting by size, shape and other attributes, the system has an accuracy rate of around 75 %. This proved how AI could transform even the smallest family-run business sooner or later.
Large companies like Google, Apple are well-aware of this transformative power. Most Fortune 500 companies also have dedicated AI teams in place. However, for smaller and medium enterprises in the same situation as Makoto, they have faced difficulties in exploring how AI can hone their business because of the lack of expertise.
Those companies are in need of preparing huge data sets and spend putting their sources into computing power to analyze them, although having ability to employ AI experts However, the big cloud providers come up with solutions to these issues.
As a service or cloud AI, Machine learning is now a major part of cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and IBM Cloud. To add AI to business applications, these companies supply their customers with access to pretrained deep learning models—for image recognition. Simplifying the process of building, training and deploying, tools customized models on the cloud.
From Chris Nicholson’s judgment - CEO of Skymind, there are tools data scientists knowing how to code and for software developers not knowing how to properly tune algorithms but knowing to create apps if receiving an API to code against. In addition to this, there are tools for clickers basically relating through GUIs, which covers the enormous number of people in all over the world.
While the likes of Amazon Rekognition and Google Translation are APIs created around pretrained models, Microsoft Azure ML Studio, Amazon SageMaker, and Google Cloud ML Engine are broadly similar platforms sitting more toward the data scientist end of the spectrum, helping deep learning experts to train, tune, and deploy their models at scale.
To solve a specific business problem, which pretrained models may cannot tackle, deep learning is typically used. This is the issue of the latter approach. Put it in another way, if you want it to identify different types of cucumbers, using an API to recognize different breeds of kittens is bad.
Now you can use a bunch of data on which we trained a model to make predictions about images but that’s a false solution in the sense that it’s still just as hard and just as necessary to train a model on your own data if wanting to customize that solution.
To bridge the gap between highly customized neural networks and the more basic one-size-fits-all pretrained models, launched by Google, Cloud AutoML uses customer data to automatically create a custom deep learning model. Allowing users to built custom machine learning models for image recognition via a drag-and-drop interface, Cloud AutoML Vision is the first release from the new service.
Disney is one of large companies using the tool and it helps Disney’s customers to search its merchandise for particular Disney characters, even the product with no the character’s name. However, preparing their own data for the AutoML service is still a difficulty for some enterprises.
Link:There could be a challenge for companies to gather data since in the first place, many of them don’t take control over their data well. A great deal of data is specific to an organization, like how they handle invoices or how they do customer lead checking.
Machine learning experience for computer engineers
This is the time to deep learning in the cloud
Machine Learning and Deep Learning In The Cloud: Are You Ready?
To attract more customers, the company is definitely trying to leverage its AI expertise as Google’s cloud business lies a distant third behind AWS and Microsoft Azure. But given the computational resources needed to train and deploy deep learning models, all the major cloud vendors stand to make considerable sums from renting chips for this purpose.
The proportion of commercial enterprise using AI will be 75% by 2021 according to IDC. There is no doubt that Machine learning tools will become an essential component of any cloud computing service. To make the most of AI, companies need to offer these kinds of capabilities.
Determining about whether a business case is clear for using deep learning tools is a needed task enterprises should do at first. People are much less likely to succeed If not defining the specific problem they want to solve. People are using it in a very specific way to cope with a very specific problem.
Is the cloud the key to democratizing AI?
Reviewed by thanhcongabc
on
July 03, 2018
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Reviewed by thanhcongabc
on
July 03, 2018
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